CODEWITHSUNDEEP
pythonpandas
Mr.Proper
Mr.Proper

Reputation: 23

Creating a new column in dataframe with range of values

I need to divide range of my passengers age onto 5 parts and create a new column where will be values from 0 to 4 respectively for every part(For 1 range value 0 for 2 range value 1 etc)

a = range(0,17)
b = range(17,34)
c = range(34, 51)
d = range(51, 68)
e = range(68,81)
a1 = titset.query('Age >= 0 & Age < 17')
a2 = titset.query('Age >= 17 & Age < 34')
a3 = titset.query('Age >= 34 & Age < 51')
a4 = titset.query('Age >= 51 & Age < 68')
a5 = titset.query('Age >= 68 & Age < 81')
titset['Age_bin'] = a1.apply(0 for a in  range(a))

Here what i tried to do but it does not work. I also pin dataset picture

DATASET

I expect to get result where i'll see a new column named 'Age_bin' and values 0 in it for Age from 0 to 16 inclusively, values 1 for age from 17 to 33 and other 3 rangers

Upvotes: 0

Views: 2584

Answers (2)

rcshon
rcshon

Reputation: 927

First of all, the variable a is a range object, which you are calling range(a) again, which is equivalent to range(range(0, 17)), hence the error.

Secondly, even if you fixed the above problem, you will run into an error again since .apply takes in a callable (i.e., a function be it defined with def or a lambda function).

If your goal is to assign a new column that represents the age group that each row is in, you can just filter with your result and assign them:

titset = pd.DataFrame({'Age': range(1, 81)})


a = range(0,17)
b = range(17,34)
c = range(34, 51)
d = range(51, 68)
e = range(68,81)

a1 = titset.query('Age >= 0 & Age < 17')
a2 = titset.query('Age >= 17 & Age < 34')
a3 = titset.query('Age >= 34 & Age < 51')
a4 = titset.query('Age >= 51 & Age < 68')
a5 = titset.query('Age >= 68 & Age < 81')

titset.loc[a1.index, 'Age_bin'] = 0
titset.loc[a2.index, 'Age_bin'] = 1
titset.loc[a3.index, 'Age_bin'] = 2
titset.loc[a4.index, 'Age_bin'] = 3
titset.loc[a5.index, 'Age_bin'] = 4

Or better yet, use a for loop:

age_groups = [0, 17, 34, 51, 68, 81]

for i in range(len(age_groups) - 1):
    subset = titset.query(f'Age >= {age_groups[i]} & Age < {age_groups[i+1]}')
    titset.loc[subset.index, 'Age_bin'] = i

Upvotes: 0

Learning is a mess
Learning is a mess

Reputation: 8277

Binning with pandas cut is appropriate here, try:

titset['Age_bin'] = titset['Age'].cut(bins=[0,17,34,51,68,81], include_lowest=True, labels=False)

Upvotes: 1

Related Questions